enhance-llm-logic-analysis

What problem does it solve?

This Skill makes opaque LLM outputs transparent by forcing structured, step-by-step reasoning and producing JSON-formatted reasoning chains so users can inspect, verify, and iterate on complex answers.

Core Features & Use Cases

• Structured Reasoning Output: Emits a sequence of titled JSON steps with detailed content and a next_action flag to indicate whether to continue or provide a final answer.
• Multi-Method Verification: Requires at least three distinct methods of analysis and explicit identification of potential failure modes and alternative answers.
• Use Case: Ideal for technical problem solving, research verification, debugging complex logic, and situations where auditors or collaborators must follow each reasoning step.

Quick Start

Ask the assistant to explain its reasoning step by step for the problem and output each step as a JSON object containing title, content, and next_action.